RAG (retrieval-augmented generation) usually needs a big-context model, and the hosted ones cost money. I wanted to see if a genuinely free endpoint could carry a real pipeline. It can — Alibaba's Qwen3.8-Max runs 1M tokens of context for $0 forever on APIShare.
What I built
A doc-chat bot over a local folder of PDFs: chunk documents (~800 tokens each) with overlap, embed with a free embedding model into pgvector, retrieve top-k, stuff them into a prompt, then call Qwen3.8-Max through APIShare's OpenAI-compatible endpoint.
The 1M context meant I could skip re-ranking entirely during dev — just throw the whole retrieved set in and it stayed coherent.
Why the free tier actually held up
Most free LLM APIs have silent rate-limits and quota cliffs. APIShare re-tests every free tier daily (latency, uptime, rate limits) and ranks them, which is how I landed on Qwen3.8-Max. The promo endpoint is: $0, no credit card, sign up and go; 1M-token context (a flagship-tier model); capped at 2 requests/min/user so it stays free for everyone.
For a personal RAG project that's more than enough — my dev loop is well under 2/min.
The one gotcha
The rpm=2 cap means you must queue calls. I wrapped the OpenAI client with a simple token-bucket limiter and a retry-on-429. Two minutes of extra code, zero dropped requests after that.
Verdict
If you're learning RAG or running a side project and don't want a monthly bill, a $0 1M-context model with a fair rate limit is a great trade. Live endpoint: https://apishare.cc/market/ddee6a9ec88d9086c193585ca346884f
I keep the free-API leaderboard bookmarked so I know when a tier degrades: https://apishare.cc
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